A Course in Interacting Particle Systems J.M. Swart January 14, 2020 arXiv:1703.10007v2 [math.PR] 13 Jan 2020 2 Contents 1 Introduction 7 1.1 General set-up . .7 1.2 The voter model . .9 1.3 The contact process . 11 1.4 Ising and Potts models . 14 1.5 Phase transitions . 17 1.6 Variations on the voter model . 20 1.7 Further models . 22 2 Continuous-time Markov chains 27 2.1 Poisson point sets . 27 2.2 Transition probabilities and generators . 30 2.3 Poisson construction of Markov processes . 31 2.4 Examples of Poisson representations . 33 3 The mean-field limit 35 3.1 Processes on the complete graph . 35 3.2 The mean-field limit of the Ising model . 36 3.3 Analysis of the mean-field model . 38 3.4 Functions of Markov processes . 42 3.5 The mean-field contact process . 47 3.6 The mean-field voter model . 49 3.7 Exercises . 51 4 Construction and ergodicity 53 4.1 Introduction . 53 4.2 Feller processes . 54 4.3 Poisson construction . 63 4.4 Generator construction . 72 4.5 Ergodicity . 79 4.6 Application to the Ising model . 81 4.7 Further results . 85 5 Monotonicity 89 5.1 The stochastic order . 89 5.2 The upper and lower invariant laws . 94 5.3 The contact process . 97 5.4 Other examples . 100 3 4 CONTENTS 5.5 Exercises . 101 6 Duality 105 6.1 Introduction . 105 6.2 Additive systems duality . 106 6.3 Cancellative systems duality . 113 6.4 Other dualities . 117 6.5 Invariant laws of the voter model . 120 6.6 Homogeneous invariant laws . 122 6.7 Equality of critical points . 128 7 Oriented percolation 131 7.1 Introduction . 131 7.2 Oriented percolation . 132 7.3 Survival . 135 7.4 K-dependence . 137 CONTENTS 5 Preface Interacting particle systems, in the sense we will be using the word in these lecture notes, are countable systems of locally interacting Markov processes. Each interacting particle system is define on a lattice: a countable set with (usually) some concept of distance defined on it; the canonical choice is the d-dimensional integer lattice Zd. On each point in this lattice, there is situated a continuous-time Markov process with a finite state space (often even of cardinality two) whose jump rates depend on the states of the Markov processes on near-by sites. Interacting particle systems are often used as extremely simplified `toy models' for stochastic phenomena that involve a spatial structure. Although the definition of an interacting particle system often looks very simple, and problems of existence and uniqueness have long been settled, it is often surprisingly difficult to prove anything nontrivial about its behavior. With a few exceptions, explicit calculations tend not to be feasible, so one has to be satisfied with qualitative statements and some explicit bounds. Despite intensive research for over more than forty years, some easy-to-formulate problems still remain open while the solutions of others have required the development of nontrivial and complicated techniques. Luckily, as a reward for all this, it turns out that despite their simple rules, interacting particle systems are often remarkably subtle models that capture the sort of phenomena one is interested in much better than might initially be expected. Thus, while it may seem outrageous to assume that \Plants of a certain type occupy points in the square lattice Z2, live for an exponential time with mean one, and place seeds on unoccupied neighboring sites with rate λ" it turns out that making the model more realistic often does not change much in its overall behavior. Indeed, there is a general philosophy in the field, that is still unsufficiently understood, that says that interacting particle systems come in `universality classes' with the property that all models in one class have roughly the same behavior. As a mathematical discipline, the subject of interacting particle systems is still relatively young. It started around 1970 with the work of R.L. Dobrushin and F. Spitzer,, with many other authors joining in during the next few years. By 1975, general existence and uniqueness questions had been settled, four classic models had been introduced (the exclusion process, the stochastic Ising model, the voter model and the contact process), and elementary (and less elementary) properties of these models had been proved. In 1985, when Liggett's published his famous book [Lig85], the subject had established itself as a mature field of study. Since then, it has continued to grow rapidly, to the point where it is impossible to accurately capture the state of the art 6 CONTENTS in a single book. Indeed, it would be possible to write a book on each of the four classic models mentioned above, while many new models have been introduced and studied. While interacting particle systems, in the narrow sense indicated above, have apparently not been the subject of mathematical study before 1970, the subject has close links to some problems that are considerably older. In particular, the Ising model (without time evolution) has been studied since 1925 while both the Ising model and the contact process have close connections to percolation, which has been studied since the late 1950-ies. In recent years, more links between interacting particle systems and other, older subjects of mathematical research have been established, and the field continues to recieve new impulses not only from the applied, but also from the more theoretical side. The present notes are loosely based on an older set of lecture notes for courses that I gave at Charles University in Prague in 2009 and 2011. An- other imput came from slides for a course I gave at Verona University in 2014. Compared to the lecture notes of 2011, most of the text has been rewritten. Many figures have been added, as well as a chapter on the mean- field limit. The old lecture notes were organized around three classical mod- els: the contact process, the Ising model, and the voter model. Instead, the present notes are organized around methods: the mean-field limit, graphical representations, monotone coupling, duality, and comparison with oriented percolation. Compared to the older notes, some results have been removed, in particular about the Ising model, whose study requires rather different techniques from the other models. Another omission are positive correla- tions. On the other hand, a wide range of interacting particle systems not (or barely) mentioned in the previous lecture notes are now used as examples throughout the notes, to give a better impression of the modern literature of the subject. I am indebted to Tibor Mach for a careful reading of the lecture notes from 2011 that led to a large number of typoes being corrected. For all new errors introduced in the present text only I am responsible. Chapter 1 Introduction 1.1 General set-up Let S be a finite set, called the local state space, and let Λ be a countable set, called the lattice. We let SΛ denote the Carthesian product space of Λ copies of S, i.e., elements x of SΛ are of the form x = x(i) i2Λ with x(i) 2 S 8 i 2 Λ: Equivalently, SΛ is nothing else than the set of all functions x :Λ ! S. Interacting particle systems are continuous-time Markov processes X = Λ (Xt)t≥0 with a state space of the form S , that are defined in terms of local maps. Thus, (Xt)t≥0 is a Markov process such that at each time t ≥ 0, the state of X is of the form Xt = Xt(i) i2Λ with Xt(i) 2 S 8 i 2 Λ: We call Xt(i) the local state of X at time t and at the position i. Positions i 2 Λ are also often called sites. The time evolution of continuous-time Markov processes is usually char- acterized by their generator G, which is an operator acting on functions f : S! R, where S is the state space. For example, in the case of Brownian motion, the state space is R and the generator is the differential operator 1 @2 G = 2 @x2 . In the case of an interacting particle system, the state space is of the form S = SΛ and the generator takes the form X Λ Gf(x) = rm f m(x) − f x (x 2 S ): (1.1) m2G Λ Λ Here G is a set whose elements are local maps m : S ! S and (rm)m2G is a collection of nonnegative constants called rates, that say with which 7 8 CHAPTER 1. INTRODUCTION Poisson intensity the local map m should be applied to the configuration Xt. The precise definitions will be given in later chapters, but at the moment it suffices to say that if we approximate (Xt)t≥0 by a discrete-time Markov chain where time is increased in steps of size dt, then rm dt is the probability that the map m is applied during the time interval (t; t + dt]. Often, the lattice Λ has the structure of an (undirected) graph. In this case, we let E denote the corresponding edge set, i.e., a set of unordered pairs fi; jg called edges, with i; j 2 Λ, i 6= j, that in drawings of the graph are connected by a line segment. We let E := (i; j): fi; jg 2 E denote the corresponding set of all ordered pairs (i; j) that correspond to an edge. We call Ni := j 2 Λ: fi; jg 2 E (1.2) the neighborhood of the site i.